No abstract

Clear cell odontogenic carcinoma is a rare, aggressive neoplasm of the jaw with only 74 reported cases. It occurs predominantly in the mandibular anterior region during fifth to seventh decades of life. Clinically it manifests as intra-bony swelling with a variable degree of pain. Microscopically, it reveals nests of cells with clear cytoplasm in connective tissue stroma arranged in different patterns. It is often misdiagnosed due to the rarity of lesion and confusing histopathology. Immunohistochemical staining plays an intricate role to uncertain the native of the clear cell to reach a confirmative diagnosis. The article aims to highlight the clinicopathologic features of clear cell odontogenic carcinoma in a middle-aged man with special emphasis on its differential diagnosis.

Calcifying odontogenic cyst (COC) is a term used broadly to define lesions which were either cystic/solid in nature. However, a new term defining dentinogenic ghost cell tumor (DGCT), as its neoplastic counterpart, histopathologically showed the presence of dentinoid-like areas, ghost cells and ameloblastomatous-like odontogenic epithelium. This possesses a great challenge to an oral pathologist in diagnosing and differentiating it from solid multicystic ameloblastoma or COCs so as to ensure the biological behavior and pathogenesis behind its multifaceted nature. The author presents an exceptional case of DGCT, with special emphasis on its pathogenesis, occurring in an 80-year-old female with facial asymmetry and unique histopathology.

Background: Implementing an active system to identify, monitor and manage risk from laboratory errors can enhance patient safety and quality of care. Aims and Objectives: Failure Mode and Effect Analysis (FMEA) technique allows evaluating and measuring the hazards of a process malfunction, to decide where to execute improvement actions, and to measure the outcome of those actions. The aim of this study was to assess pre analytical phase of laboratory testing, mitigate risk and thereby increase patient safety. Materials and Methods: Steps followed in the study were: planning the study, selecting team members, analysis of the processes, risk analysis, and developing a risk reduction protocol by incorporating corrective actions. A Fault Tree Analysis diagram was used to plot the cascade of faults leading to the pre analytical errors. Risk Priority Number (RPN) was assigned. A minimum cut- off 40 RPN was considered for interventions and highest RPN errors were prioritized with corrective actions. Post intervention RPN score was calculated. Results: Eight failure modes had the highest RPN. Corrective actions were prioritized against these errors. RPN scores of test ordering error, sample collection error, transport errors, error in patient identification, site selection, urine samples not received, sample accessioning and sample processing errors decreased, post intervention. Conclusion: With thorough planning, we can use FMEA as a common standard to analyze risk in pre analytical phase of laboratory testing.